Moderate deviations and hypothesis testing for signal detection problem

Abstract

We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the moderate deviation estimates to hypothesis testing for signal detection problem we give a decision region such that its error probability of the second kind tends to zero with faster speed than the error probability of the first kind when the error probability of the first kind is approximated by e −αr (T), where α > 0, r(T) = o(T) and r(T)→∞ as the observation time T goes to infinity.